The Book of Why: The New Science of Cause and Effect
Judea Pearl and Dana Mackenzie
Basic Books (May 2018)

Perhaps a “new science”: You decide.

What is causal inference and why is it important? According to Judea Pearl and Dana Mackenzie, it is a “machine” driven by an engine that enables it to accept three kinds of inputs (Assumptions, Queries, and Data) and produces three kinds of outputs: “The first is a Yes/No decision as to whether the given query can be answered under the existing causal model, assuming perfect and unlimited data.

If the answer is Yes, the inference engine next produces an Estimand. This is a mathematical formula that can be   thought of as a recipe [or perhaps — as I prefer — an algorithm] for generating the answer from any hypothetical data, whenever they are available.

Finally, after the inference engine has received the Data input, it will use the recipe to produce an actual Estimate for the answer, along with the statistical estimates of the amount of uncertainty in that estimate. The uncertainty reflects the limited size of the data set as well as possible measurement errors or missing data.”

So what?

Pearl and Mackenzie are convinced that humans are smarter than their data because data do not understand causes and effects; humans do. They wrote this book to explain the new science of causal inference so that humans have an increased understanding of causes and effects “because there is no better way to understand ourselves than by emulating ourselves. In the age of computers, this new understanding brings with it the prospect of amplifying our innate abilities so that we can make better sense of data, be it big or small.”

These are among the passages in The Book of Why that are of greatest interest and value to me, also listed to suggest the scope of Pearl and Mackenzie’s coverage:

o A Blueprint of Reality (Pages 11-17)
o The Three Levels of Causation (27-36)
o On Probabilities and Causation (47-51)
o [Francis] Galton and the Abandoned Quest (63-66)
o [Egon] Pearson: The Wrath of the Zealot (66-72)

o From Objectivity to Subjectivity — The Baynesian Connection (88-91)
o Reverend Bayes and the Problem of Inverse Probability (95-108)
o Baynesian Networks: What Causes Say About Data (112-116)
o Baynesian Networks in the Real World (122-128)
o The Chilling Fear of Confounding (141-143)

o The New Paradigm of Confounding (150-157)
o Thed Perplexing Monty Hall Problem (190-197)
o The Simplest Route: The Back-Door Adjustment Formula (220-224)
o The Do-Calculus, or Mind Over Matter (231-242)
o From Thrucidides and Abraham to Hume and Lewis (262-269)

o The Virtue of Seeing Your Assumptions (280-286)
o Counterfactuals and the Law (286-291)
o Necessary Causes, Sufficient Causes, and Climate Change (291-296)
o Nature Versus Nurture: The Tragedy of Barbara Burks (304-309)
o Case Studies of Mediation (336-347)

Judea Pearl and Dana Mackenzie wrote this book in order to “lay before you in nonmathematical language the intellectual content of the Causal Revolution and how it is affecting our lives as well as our future… to share with you some of the heroic journeys, both successful and failed, that scientists have embarked on when confronted by critical cause-effect questions… [and] to describe to you how robots can be constructed that learn to communicate in our mother tongue – the language of cause and effect.”

I am unqualified to agree or disagree that there is (or was or will be) a Causal Revolution or that the language of cause and effect is our mother tongue. However, I am convinced that gaining a better understanding of what causal inference is and how to use it more effectively will help business leaders to improve problem solving by sharpening their focus on root causes rather than on symptoms.